AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want
AI on animals: AI-assisted animal-borne logger never misses the moments that biologists want
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DOI：
10.1101/630053
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发表时间：
2019-05
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影响因子：
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通讯作者：
Joseph Korpela;Hirokazu Suzuki;Sakiko Matsumoto;Y. Mizutani;Masaki Samejima;Takuya Maekawa;Junichi Nakai;Ken Yoda
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文献类型：
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作者：
Joseph Korpela;Hirokazu Suzuki;Sakiko Matsumoto;Y. Mizutani;Masaki Samejima;Takuya Maekawa;Junichi Nakai;Ken Yoda
动物携带的数据记录器，即，生物记录仪使研究人员能够记录自然环境中动物的各种传感器数据（Hussey et al. 2015; Kays et al. 2015）。这些数据使生物学家能够观察动物生活的许多方面，包括它们的行为，生理，社会互动和外部环境。然而，需要将这些设备的尺寸限制在动物尺寸的一小部分，这对设备的硬件和电池容量施加了严格的限制（Kays等人，2015）。在这里，我们展示了如何在这些设备上利用人工智能来智能地控制昂贵传感器的激活，例如，摄像机，使他们能够在长期部署期间充分利用有限的资源。我们的方法超越了以前的工作，这些工作提出了使用简单的基于阈值的触发器来控制这种昂贵的传感器，例如，基于深度（Watanuki et al. 2007; Volpov et al. 2015）和基于加速度（Nishiumi et al. 2018; Brown et al. 2012）的触发器。使用人工智能辅助的生物记录器，生物学家可以将他们的数据收集集中在特定的复杂目标行为上，例如觅食活动，使他们能够自动记录只捕捉他们想要看到的时刻的视频。通过这样做，生物记录器可以保留其电池电量，仅用于记录那些目标活动。我们预计，我们的工作将为生物记录仪更广泛地采用人工智能技术提供动力，包括智能传感器控制和智能机载数据处理。这些技术不仅可以用于控制这些设备收集的内容，还可以控制从设备传输的内容，例如通过卫星中继标签（考克斯等人。2018）。
Animal-borne data loggers, i.e., biologgers, allow researchers to record a variety of sensor data from animals in their natural environments (Hussey et al. 2015; Kays et al. 2015). This data allows biologists to observe many aspects of the animals’ lives, including their behavior, physiology, social interactions, and external environment. However, the need to limit the size of these devices to a small fraction of the animal’s size imposes strict limits on the devices’ hardware and battery capacities (Kays et al. 2015). Here we show how AI can be leveraged on board these devices to intelligently control their activation of costly sensors, e.g., video cameras, allowing them to make the most of their limited resources during long deployment periods. Our method goes beyond previous works that have proposed controlling such costly sensors using simple threshold-based triggers, e.g., depth-based (Watanuki et al. 2007; Volpov et al. 2015) and acceleration-based (Nishiumi et al. 2018; Brown et al. 2012) triggers. Using AI-assisted biologgers, biologists can focus their data collection on specific complex target behaviors such as foraging activities, allowing them to automatically record video that captures only the moments they want to see. By doing so, the biologger can reserve its battery power for recording only those target activities. We anticipate our work will provide motivation for more widespread adoption of AI techniques on biologgers, both for intelligent sensor control and intelligent onboard data processing. Such techniques can not only be used to control what is collected by such devices, but also what is transmitted off the devices, such as is done by satellite relay tags (Cox et al. 2018).